As precision agriculture evolves,unmanned ground vehicles(UGVs)have become an essential tool for improving weed management techniques,offering automated and targeted methods that obviously reduce the reliance on manua...As precision agriculture evolves,unmanned ground vehicles(UGVs)have become an essential tool for improving weed management techniques,offering automated and targeted methods that obviously reduce the reliance on manual labor and blanket herbicide applications.Several papers on UGV-based weed control methods have been published in recent years,yet there is no explicit attempt to systematically study these papers to discuss these weed control methods,UGVs adopted,and their key components,and how they impact the environment and economy.Therefore,the objective of this study was to present a systematic review that involves the efficiency and types of weed control methods deployed in UGVs,including mechanical weeding,targeted herbicide application,thermal/flaming weeding,and laser weeding in the last 2 decades.For this purpose,a thorough literature review was conducted,analyzing 68 relevant articles on weed control methods for UGVs.The study found that the research focus on using UGVs in mechanical weeding has been more dominant,followed by target or precision spraying/chemical weeding,with hybrid weeding systems quickly emerging.The effectiveness of UGVs for weed control is hinged on the accuracy of their navigation and weed detection technologies,which are influenced heavily by environmental conditions,including lighting,weather,uneven terrain,and weed and crop density.Also,there is a shift from using traditional machine learning(ML)algorithms to deep learning neural networks,including convolutional neural networks(CNNs)and recurrent neural networks(RNNs),for weed detection algorithm development due to their potential to work in complex environments.Finally,trials of most UGVs have limited documentation or lack extensive trials under various conditions,such as varying soil types,crop fields,topography,field geometry,and annual weather conditions.This review paper serves as an in-depth update on UGVs in weed management for farmers,researchers,robotic technology industry players,and AI enthusiasts,helping to further foster collaborative efforts to develop new ideas and advance this revolutionary technique in modern agriculture.展开更多
基金supported by the U.S.Department of Agriculture,agreement number 58-3060-3-021,58-3060-3-022 and 58-3060-3-035supported in part by the intramural research program of the U.S.Department of Agriculture,National Institute of Food and Agriculture[The Agriculture and Food Research Initiative][Award#2024-67022-42001]supported by the USDA National Institute of Food and Agriculture,Hatch project number ND01487.
文摘As precision agriculture evolves,unmanned ground vehicles(UGVs)have become an essential tool for improving weed management techniques,offering automated and targeted methods that obviously reduce the reliance on manual labor and blanket herbicide applications.Several papers on UGV-based weed control methods have been published in recent years,yet there is no explicit attempt to systematically study these papers to discuss these weed control methods,UGVs adopted,and their key components,and how they impact the environment and economy.Therefore,the objective of this study was to present a systematic review that involves the efficiency and types of weed control methods deployed in UGVs,including mechanical weeding,targeted herbicide application,thermal/flaming weeding,and laser weeding in the last 2 decades.For this purpose,a thorough literature review was conducted,analyzing 68 relevant articles on weed control methods for UGVs.The study found that the research focus on using UGVs in mechanical weeding has been more dominant,followed by target or precision spraying/chemical weeding,with hybrid weeding systems quickly emerging.The effectiveness of UGVs for weed control is hinged on the accuracy of their navigation and weed detection technologies,which are influenced heavily by environmental conditions,including lighting,weather,uneven terrain,and weed and crop density.Also,there is a shift from using traditional machine learning(ML)algorithms to deep learning neural networks,including convolutional neural networks(CNNs)and recurrent neural networks(RNNs),for weed detection algorithm development due to their potential to work in complex environments.Finally,trials of most UGVs have limited documentation or lack extensive trials under various conditions,such as varying soil types,crop fields,topography,field geometry,and annual weather conditions.This review paper serves as an in-depth update on UGVs in weed management for farmers,researchers,robotic technology industry players,and AI enthusiasts,helping to further foster collaborative efforts to develop new ideas and advance this revolutionary technique in modern agriculture.
